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Powering the agentic revolution in travel. Sabre is an AI-native technology leader, backed by one of the worlds largest travel data clouds. Built on an open, modular, cloud-native architecture, Sabre serves as the backbone for both established leaders and bold, new disruptors, guiding them to the next age of travel retailing through intelligent, connected, and personalized experiences. With AI at its core and operating at unparalleled scale, Sabre transforms insights into innovation, empowering airlines, hoteliers, agencies and other partners to retail, distribute and fulfill travel worldwide. DATA SCIENCE ENGINEER Powering the agentic revolution in travel. Sabre is an AI-native technology leader, backed by one of the worlds largest travel data clouds. Built on an open, modular, cloud-native architecture, Sabre serves as the backbone for both established leaders and bold, new disruptors, guiding them to the next age of travel retailing through intelligent, connected, and personalized experiences. With AI at its core and operating at unparalleled scale, Sabre transforms insights into innovation, empowering airlines, hoteliers, agencies and other partners to retail, distribute and fulfill travel worldwide. Team Description Sabre is seeking a talented Sr. Data Science Engineer to join the Sabre Data Intelligence & Research team. Sabre Data Intelligence & Research is a specialized team of researchers, mathematicians, and AI engineers who serve as the intelligent core of Sabres travel marketplace. We are dedicated to solving the most complex challenges across the global travel domain. Our mission spans Airline Pricing, Agency Automation, Network Planning, Airline Operations, and Departure Control Systems (DCS). We build high-performance systems that streamline travel agency workflows, optimize airline networks, and enhance the productivity of travel consultants worldwide. By blending classical mathematical optimization with cutting-edge Machine Learning , Generative AI and robust MLOps , we ensure our models power the next generation of travel distributionfrom initial search and booking to post-trip service, agency efficiency, and seamless airport operations. At Sabre, were passionate about building software that solves problems. In this role, you will plan, design, develop, and test new products and solutions revelevant for the industry and Sabre, help build software systems or applications for software enhancements. Role and Responsibilities: Architects, codes, and deploys complex software solutions or data intelligence pipelines that integrate advanced mathematical algorithms and machine learning models into Sabres core retailing, Network Planning, Agency Automation, and Departure Control platforms. Drives Research & Innovation by identifying, prototyping, and scaling "next-gen" ideas within the travel domain, such as AI-driven automation for Travel Management Companies (TMCs) and personalized, real-time itinerary optimization. Designs end-to-end AI systems , specifically focusing on identifying new market opportunities in Media, integration of Large Language Models (LLMs) for various internal tools, customer intent recognition, and conversational commerce. Solves high-impact organizational challenges , such as optimizing Global Distribution System (GDS) workflows and migrating legacy automation logic into modern, GCP-based AI architectures . Establishes strategic AI lifecycle policies , defining rigorous standards for MLOps to ensure high-fidelity model performance across the volatile data landscapes of the travel industry. Promotes engineering excellence by utilizing best practices in Python, golang or C++ , ensuring research-grade prototypes are refactored into production-quality, low-latency systems. Collaborates with cross-functional leadership , including architects and product managers, to align technical roadmaps with business goals like NDC adoption, total travel cost optimization, and operational efficiency. Influences stakeholder decision-making , advocating for the adoption of disruptive technologiesincluding Agentic AIwithin mission-critical, high-availability travel environments. Mentors and develops junior data scientists and engineers, fostering a culture of rigorous experimentation, academic-level research, and high-performance engineering. Qualifications and Education Requirements: Minimum 4 years of related experience in Data Science, AI Engineering, or Operations Research. Advanced Degree (Masters/PhD preferred) in Mathematics, Machine Learning, Statistics, Computer Science, or Physics with a strong research background and a proven track record of innovation. Proven experience in Agile Software Development , with a demonstrated ability to move complex models from the research phase to global production. Expertise in advanced ML/AI solutioning , including Deep Learning, Reinforcement Learning, and
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.